A singing voice synthesis system based on sinusoidal modeling

Although sinusoidal models have been demonstrated to be capable of high-quality musical instrument synthesis, speech modification, and speech synthesis, little exploration of the application of these models to the synthesis of singing voice has been undertaken. We propose a system framework similar to that employed in concatenation-based text-to-speech synthesizers, and describe its extension to the synthesis of singing voice. The power and flexibility of the sinusoidal model used in the waveform synthesis portion of the system enables high-quality, computationally-efficient synthesis and the incorporation of musical qualities such as vibrato and spectral tilt variation. Modeling of segmental phonetic characteristics is achieved by employing a “unit selection” procedure that selects sinusoidally-modeled segments from an inventory of singing voice data collected from a human vocalist. The system, called LYRICOS, is capable of synthesizing very natural-sounding singing that maintains the characteristics and perceived identity of the analyzed vocalist